Personalized language modeling by crowd sourcing with social network data for voice access of cloud applications

Abstract

Voice access of cloud applications via smartphones is very attractive today, specifically because a smartphones is used by a single user, so personalized acoustic/language models become feasible. In particular, huge quantities of texts are available within the social networks over the Internet with known authors and given relationships, it is possible to train personalized language models because it is reasonable to assume users with those relationships may share some common subject topics, wording habits and linguistic patterns. In this paper, we propose an adaptation framework for building a robust personalized language model by incorporating the texts the target user and other users had posted on the social networks over the Internet to take care of the linguistic mismatch across different users. Experiments on Facebook dataset showed encouraging improvements in terms of both model perplexity and recognition accuracy with proposed approaches considering relationships among users, similarity based on latent topics, and random walk over a user graph.